@inproceedings{ji-etal-2025-pku,
title = "{PKU}-{S}afe{RLHF}: Towards Multi-Level Safety Alignment for {LLM}s with Human Preference",
author = "Ji, Jiaming and
Hong, Donghai and
Zhang, Borong and
Chen, Boyuan and
Dai, Josef and
Zheng, Boren and
Qiu, Tianyi Alex and
Zhou, Jiayi and
Wang, Kaile and
Li, Boxun and
Han, Sirui and
Guo, Yike and
Yang, Yaodong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1544/",
doi = "10.18653/v1/2025.acl-long.1544",
pages = "31983--32016",
ISBN = "979-8-89176-251-0",
abstract = "In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectives on these coupled attributes. Overall, we provide 44.6k refined prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels ranging from minor to severe, with answers generated by Llama-family models. Based on this, we collected 166.8k preference data, including dual-preference (helpfulness and harmlessness decoupled) and single-preference data (trade-off the helpfulness and harmlessness from scratch), respectively. Using the large-scale annotation data, we further train severity-sensitive moderation for the risk control of LLMs and safety-centric RLHF algorithms for the safety alignment of LLMs. We believe this dataset will be a valuable resource for the community, aiding in the safe deployment of LLMs."
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<abstract>In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectives on these coupled attributes. Overall, we provide 44.6k refined prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels ranging from minor to severe, with answers generated by Llama-family models. Based on this, we collected 166.8k preference data, including dual-preference (helpfulness and harmlessness decoupled) and single-preference data (trade-off the helpfulness and harmlessness from scratch), respectively. Using the large-scale annotation data, we further train severity-sensitive moderation for the risk control of LLMs and safety-centric RLHF algorithms for the safety alignment of LLMs. We believe this dataset will be a valuable resource for the community, aiding in the safe deployment of LLMs.</abstract>
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%0 Conference Proceedings
%T PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference
%A Ji, Jiaming
%A Hong, Donghai
%A Zhang, Borong
%A Chen, Boyuan
%A Dai, Josef
%A Zheng, Boren
%A Qiu, Tianyi Alex
%A Zhou, Jiayi
%A Wang, Kaile
%A Li, Boxun
%A Han, Sirui
%A Guo, Yike
%A Yang, Yaodong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F ji-etal-2025-pku
%X In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectives on these coupled attributes. Overall, we provide 44.6k refined prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels ranging from minor to severe, with answers generated by Llama-family models. Based on this, we collected 166.8k preference data, including dual-preference (helpfulness and harmlessness decoupled) and single-preference data (trade-off the helpfulness and harmlessness from scratch), respectively. Using the large-scale annotation data, we further train severity-sensitive moderation for the risk control of LLMs and safety-centric RLHF algorithms for the safety alignment of LLMs. We believe this dataset will be a valuable resource for the community, aiding in the safe deployment of LLMs.
%R 10.18653/v1/2025.acl-long.1544
%U https://aclanthology.org/2025.acl-long.1544/
%U https://doi.org/10.18653/v1/2025.acl-long.1544
%P 31983-32016
Markdown (Informal)
[PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference](https://aclanthology.org/2025.acl-long.1544/) (Ji et al., ACL 2025)
ACL
- Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Alex Qiu, Jiayi Zhou, Kaile Wang, Boxun Li, Sirui Han, Yike Guo, and Yaodong Yang. 2025. PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31983–32016, Vienna, Austria. Association for Computational Linguistics.